OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms. - Zhaoyi Meng; Alice Koniges; Yun (Helen) He; Samuel Williams 001; Thorsten Kurth; Brandon Cook 001; Jack Deslippe; Andrea L. Bertozzi (IWOMP, 2016)

MPI usage at NERSC: Present and Future. - Alice Koniges; Brandon Cook 001; Jack Deslippe; Thorsten Kurth; Hongzhang Shan (EuroMPI, 2016)

Evaluating and Optimizing the NERSC Workload on Knights Landing. - Taylor Barnes; Brandon Cook 001; Jack Deslippe; Douglas Doerfler; Brian Friesen; Yun (Helen) He; Thorsten Kurth; Tuomas Koskela; Mathieu Lobet; Tareq M. Malas; Leonid Oliker; Andrey Ovsyannikov; et al. (PMBS@SC, 2016)

Applying the Roofline Performance Model to the Intel Xeon Phi Knights Landing Processor. - Douglas Doerfler; Jack Deslippe; Samuel Williams 001; Leonid Oliker; Brandon Cook 001; Thorsten Kurth; Mathieu Lobet; Tareq M. Malas; Jean-Luc Vay; Henri Vincenti (ISC Workshops, 2016)

Optimizing Wilson-Dirac Operator and Linear Solvers for Intel® KNL. - Bálint Joó; Dhiraj D. Kalamkar; Thorsten Kurth; Karthikeyan Vaidyanathan; Aaron Walden (ISC Workshops, 2016)

Optimization of the Sparse Matrix-Vector Products of an IDR Krylov Iterative Solver in EMGeo for the Intel KNL Manycore Processor. - Tareq M. Malas; Thorsten Kurth; Jack Deslippe (ISC Workshops, 2016)

Deep learning at 15PF: supervised and semi-supervised classification for scientific data. - Thorsten Kurth; Jian Zhang 049; Nadathur Satish; Evan Racah; Ioannis Mitliagkas; Md. Mostofa Ali Patwary; Tareq M. Malas; Narayanan Sundaram; Wahid Bhimji; Mikhail Smorkalov; Jack Deslippe; Mikhail Shiryaev; et al. (SC, 2017)

Performance Variability on Xeon Phi. - Brandon Cook 001; Thorsten Kurth; Brian Austin; Samuel Williams 001; Jack Deslippe (ISC Workshops, 2017)

Analyzing Performance of Selected NESAP Applications on the Cori HPC System. - Thorsten Kurth; William Arndt; Taylor Barnes; Brandon Cook 001; Jack Deslippe; Douglas Doerfler; Brian Friesen; Yun (Helen) He; Tuomas Koskela; Mathieu Lobet; Tareq M. Malas; Leonid Oliker; et al. (ISC Workshops, 2017)

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. - Wahid Bhimji; Steven Andrew Farrell; Thorsten Kurth; Michela Paganini; Prabhat; Evan Racah (CoRR, 2017)

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer. - Amrita Mathuriya; Thorsten Kurth; Vivek Rane; Mustafa Mustafa; Lei Shao; Debbie Bard; Prabhat; Victor W. Lee (CoRR, 2017)

Improved treatment of exact exchange in Quantum ESPRESSO. - Taylor Barnes; Thorsten Kurth; Pierre Carrier; Nathan Wichmann; David Prendergast 002; Paul R. C. Kent; Jack Deslippe (Comput. Phys. Commun., 2017)

A Metric for Evaluating Supercomputer Performance in the Era of Extreme Heterogeneity. - Brian Austin; Christopher S. Daley; Douglas Doerfler; Jack Deslippe; Brandon Cook 001; Brian Friesen; Thorsten Kurth; Charlene Yang; Nicholas J. Wright (PMBS@SC, 2018)

Simulating the weak death of the Neutron in a femtoscale universe with near-exascale computing. - Evan Berkowitz; Michael A. Clark; Arjun Singh Gambhir; Kenneth McElvain; Amy N. Nicholson; Enrico Rinaldi; Pavlos Vranas; André Walker-Loud; Chia-Cheng Chang; Bálint Joó; Thorsten Kurth; Kostas Orginos (SC, 2018)

A Case Study for Performance Portability Using OpenMP 4.5. - Rahulkumar Gayatri; Charlene Yang; Thorsten Kurth; Jack Deslippe (WACCPD@SC, 2018)

Exascale deep learning for climate analytics. - Thorsten Kurth; Sean Treichler; Joshua Romero; Mayur Mudigonda; Nathan Luehr; Everett H. Phillips; Ankur Mahesh; Michael A. Matheson; Jack Deslippe; Massimiliano Fatica; Prabhat; Michael Houston (SC, 2018)

Sparse CSB_Coo Matrix-Vector and Matrix-Matrix Performance on Intel Xeon Architectures. - Brandon Cook 001; Charlene Yang; Thorsten Kurth; Jack Deslippe (ISC Workshops, 2018)

Lessons Learned from Optimizing Kernels for Adaptive Aggregation Multi-grid Solvers in Lattice QCD. - Bálint Joó; Thorsten Kurth (ISC Workshops, 2018)

Preparing NERSC users for Cori, a Cray XC40 system with Intel many integrated cores. - Yun (Helen) He; Brandon Cook 001; Jack Deslippe; Brian Friesen; Richard A. Gerber; Rebecca Hartman-Baker; Alice Koniges; Thorsten Kurth; Stephen Leak; Woo-Sun Yang; Zhengji Zhao; Eddie Baron; et al. (Concurr. Comput. Pract. Exp., 2018)

Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing. - Evan Berkowitz; Michael A. Clark; Arjun Singh Gambhir; Kenneth McElvain; Amy N. Nicholson; Enrico Rinaldi; Pavlos Vranas; André Walker-Loud; Chia-Cheng Chang; Bálint Joó; Thorsten Kurth; Kostas Orginos (CoRR, 2018)

A per-cent-level determination of the nucleon axial coupling from quantum chromodynamics. - Chin-Chen Chang 001; Amy N. Nicholson; Enrico Rinaldi; Evan Berkowitz; Nicolas Garron; D. A. Brantley; H. Monge-Camacho; C. J. Monahan; Chris Bouchard; Michael A. Clark; Bálint Joó; Thorsten Kurth; et al. (Nat., 2018)

PCS: A Productive Computational Science Platform. - David Ojika; Ann Gordon-Ross; Herman Lam; Shinjae Yoo; Younggang Cui; Zhihua Dong; Kirstin Kleese van Dam; Seyong Lee; Thorsten Kurth (HPCS, 2019)

Performance Portability of a Wilson Dslash Stencil Operator Mini-App Using Kokkos and SYCL. - Bálint Joó; Thorsten Kurth; Michael A. Clark; Jeongnim Kim; Christian Robert Trott; Dan Ibanez; Daniel Sunderland; Jack Deslippe (P3HPC@SC, 2019)

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. - Liu Yang; Prabhat; George E. Karniadakis; Sean Treichler; Thorsten Kurth; Keno Fischer; David A. Barajas-Solano; Joshua Romero; Valentin Churavy; Alexandre M. Tartakovsky; Michael Houston (DLS@SC, 2019)

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs. - Chao Jiang; Dave Ojika; Thorsten Kurth; Prabhat; Sofia Vallecorsa; Bhavesh Patel; Herman Lam (ISC Workshops, 2019)

Eigensolver performance comparison on Cray XC systems. - Brandon Cook 001; Thorsten Kurth; Jack Deslippe; Pierre Carrier; Nick Hill; Nathan Wichmann (Concurr. Comput. Pract. Exp., 2019)

TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML. - Thorsten Kurth; Mikhail Smorkalov; Peter Mendygral; Srinivas Sridharan 002; Amrita Mathuriya (Concurr. Comput. Pract. Exp., 2019)

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. - Liu Yang; Sean Treichler; Thorsten Kurth; Keno Fischer; David A. Barajas-Solano; Joshua Romero; Valentin Churavy; Alexandre M. Tartakovsky; Michael Houston; Prabhat; George E. Karniadakis (CoRR, 2019)

Time-Based Roofline for Deep Learning Performance Analysis. - Yunsong Wang; Charlene Yang; Steven Farrell; Yan Zhang; Thorsten Kurth; Samuel Williams 001 (DLS@SC, 2020)

Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC-9 Perlmutter system. - Charlene Yang; Thorsten Kurth; Samuel Williams 001 (Concurr. Comput. Pract. Exp., 2020)

Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations. - Jaideep Pathak; Mustafa Mustafa; Karthik Kashinath; Emmanuel Motheau; Thorsten Kurth; Marcus Day (CoRR, 2020)

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. - Aymen Al Saadi; Dario Alfè; Yadu N. Babuji; Agastya Bhati; Ben Blaiszik; Alexander Brace; Thomas S. Brettin; Kyle Chard; Ryan Chard; Austin Clyde; Peter V. Coveney; Ian T. Foster; et al. (ICPP, 2021)

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (MLHPC@SC, 2021)

Hierarchical Roofline Performance Analysis for Deep Learning Applications. - Charlene Yang; Yunsong Wang; Thorsten Kurth; Steven Farrell; Samuel Williams 001 (SAI (2), 2021)

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (CoRR, 2021)

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. - Lorenzo Casalino; Abigail C. Dommer; Zied Gaieb; Emília P. Barros; Terra Sztain; Surl-Hee Ahn; Anda Trifan; Alexander Brace; Anthony T. Bogetti; Austin Clyde; Heng Ma; Hyungro Lee; et al. (Int. J. High Perform. Comput. Appl., 2021)

NERSC Job Script Generator. - Robin Shao; Thorsten Kurth; Zhengji Zhao (HUST@SC, 2022)

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. - Jaideep Pathak; Shashank Subramanian; Peter Harrington; Sanjeev Raja; Ashesh Chattopadhyay; Morteza Mardani; Thorsten Kurth; David Hall; Zongyi Li; Kamyar Azizzadenesheli; Pedram Hassanzadeh; Karthik Kashinath; et al. (CoRR, 2022)

Clinical validation of rapid GPU-enabled DTI tractography of the brain. - Felix Liu; Vanitha Sankaranarayanan; Javier E. Villanueva-Meyer; Shawn Hervey-Jumper; James Hawkins; Pablo F. Damasceno; Mauro Bisson; Joshua Romero; Thorsten Kurth; Massimiliano Fatica; Eleftherios Garyfallidis; Ariel Rokem; et al. (High Performance Computing for Imaging, 2023)

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. - Boris Bonev; Thorsten Kurth; Christian Hundt 002; Jaideep Pathak; Maximilian Baust; Karthik Kashinath; Anima Anandkumar (ICML, 2023)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. - Thorsten Kurth; Shashank Subramanian; Peter Harrington; Jaideep Pathak; Morteza Mardani; David Hall; Andrea Miele; Karthik Kashinath; Anima Anandkumar (PASC, 2023)

Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks. - Aristeidis Tsaris; Joshua Romero; Thorsten Kurth; Jacob D. Hinkle; Hong-Jun Yoon; Feiyi Wang; Sajal Dash; Georgia D. Tourassi (PASC, 2023)

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. - Sungduk Yu; Walter M. Hannah; Liran Peng; Mohamed Aziz Bhouri; Ritwik Gupta; Jerry Lin; Björn Lütjens; Justus C. Will; Tom Beucler; Bryce E. Harrop; Benjamin R. Hillman; Andrea M. Jenney; et al. (CoRR, 2023)

Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh. - Matthias Karlbauer; Nathaniel Cresswell-Clay; Raul A. Moreno; Dale R. Durran; Thorsten Kurth; Martin V. Butz (CoRR, 2023)

Neural Operators with Localized Integral and Differential Kernels. - Miguel Liu-Schiaffini; Julius Berner; Boris Bonev; Thorsten Kurth; Kamyar Azizzadenesheli; Anima Anandkumar (ICML, 2024)

Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression. - Hatem Ltaief; Rabab Alomairy; Qinglei Cao; Jie Ren; Lotfi Slim; Thorsten Kurth; Benedikt Dorschner; Salim Bougouffa; Rached Abdelkhalak; David E. Keyes (SC, 2024)

A Practical Probabilistic Benchmark for AI Weather Models. - Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Ankur Mahesh; Boris Bonev; Thorsten Kurth; Dale R. Durran; Peter Harrington; Michael S. Pritchard (CoRR, 2024)

Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model. - Chenggong Wang; Michael S. Pritchard; Noah D. Brenowitz; Yair Cohen; Boris Bonev; Thorsten Kurth; Dale R. Durran; Jaideep Pathak (CoRR, 2024)

Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators. - Ankur Mahesh; William D. Collins; Boris Bonev; Noah D. Brenowitz; Yair Cohen; Peter Harrington; Karthik Kashinath; Thorsten Kurth; Joshua North; Travis A. O'Brien; Michael S. Pritchard; David Pruitt; et al. (CoRR, 2024)

Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators. - Ankur Mahesh; William D. Collins; Boris Bonev; Noah D. Brenowitz; Yair Cohen; Joshua Elms; Peter Harrington; Karthik Kashinath; Thorsten Kurth; Joshua North; Travis A. O'Brien; Michael S. Pritchard; et al. (CoRR, 2024)

Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts. - Jussi Leinonen; Boris Bonev; Thorsten Kurth; Yair Cohen (CoRR, 2024)

Attention on the Sphere. - Boris Bonev; Max Rietmann; Andrea Paris; Alberto Carpentieri; Thorsten Kurth (CoRR, 2025)

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale. - Boris Bonev; Thorsten Kurth; Ankur Mahesh; Mauro Bisson; Jean Kossaifi; Karthik Kashinath; Anima Anandkumar; William D. Collins; Michael S. Pritchard; Alexander Keller (CoRR, 2025)